Papers
Topics
Authors
Recent
Search
2000 character limit reached

Deep Learning as a Method for Inversion of NMR Signals

Published 22 Nov 2023 in physics.chem-ph, cs.LG, and eess.IV | (2311.13722v1)

Abstract: The concept of deep learning is employed for the inversion of NMR signals and it is shown that NMR signal inversion can be considered as an image-to-image regression problem, which can be treated with a convolutional neural net. It is further outlined, that inversion through deep learning provides a clear efficiency and usability advantage compared to regularization techniques such as Tikhonov and modified total generalized variation (MTGV), because no hyperparemeter selection prior to reconstruction is necessary. The inversion network is applied to simulated NMR signals and the results compared with Tikhonov- and MTGV-regularization. The comparison shows that inversion via deep learning is significantly faster than the latter regularization methods and also outperforms both regularization techniques in nearly all instances.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (50)
  1. Alex Krizhevsky, Ilya Sutskever and Geoffrey E Hinton “ImageNet Classification with Deep Convolutional Neural Networks” In Communications of the ACM 60.6, 2017, pp. 84–90 DOI: 10.1145/3065386
  2. “Going Deeper with Convolutions” In IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9 DOI: 10.1109/cvpr.2015.7298594
  3. “Deep Residual Learning for Image Recognition” In arXiv, 2015 DOI: 10.48550/arXiv.1512.03385
  4. “Recurrent Neural Network Based Language Model” In Interspeech, 2010, pp. 1045–1048 DOI: 10.21437/interspeech.2010-343
  5. “Extensions of Recurrent Neural Network Language Model” In IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, pp. 5528–5531 DOI: 10.1109/ICASSP.2011.5947611
  6. “Deep Neural Networks for YouTube Recommendations” In Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp. 191–198 DOI: 10.1145/2959100.2959190
  7. “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification” In arXiv, 2015 DOI: 10.48550/arXiv.1502.01852
  8. Alex Krizhevsky “One Weird Trick for Parallelizing Convolutional Neural Networks” In arXiv, 2014 DOI: 10.48550/arXiv.1404.5997
  9. “Scalable and Accurate Deep Learning with Electronic Health Records” In NPJ Digital Medicine 1.1, 2018, pp. 18 DOI: 10.1038/s41746-018-0029-1
  10. “Privacy-Preserving Deep Learning” In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 2015, pp. 1310–1321 DOI: 10.1145/2810103.2813687
  11. “Learning Long-Term Dependencies in NARX Recurrent Neural Networks” In IEEE Transactions on Neural Networks 7.6, 1996, pp. 1329–1338 DOI: 10.1109/72.548162
  12. “Image-to-Image Translation with Conditional Adversarial Networks” In IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5967–5976 DOI: 10.1109/cvpr.2017.632
  13. M. Raissi, P. Perdikaris and G.E. Karniadakis “Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations” In Journal of Computational Physics 378, 2019, pp. 686–707 DOI: 10.1016/j.jcp.2018.10.045
  14. “OrbNet: Deep learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features” In Journal of Chemical Physics 153.12, 2020, pp. 124111 DOI: 10.1063/5.0021955
  15. Raissi Maziar “Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations” In Journal of Machine Learning Research 19, 2018, pp. 1–24
  16. “Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning” In Angewandte Chemie International Edition 59.26, 2020, pp. 10297–10300 DOI: 10.1002/anie.201908162
  17. “ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition” In Scientific Reports 8.1, 2018, pp. 17593 DOI: 10.1038/s41598-018-35934-y
  18. “Detecting and Classifying Lesions in Mammograms with Deep Learning” In Scientific Report 8.1, 2018, pp. 4165 DOI: 10.1038/s41598-018-22437-z
  19. “Highly Accurate Protein Structure Prediction with AlphaFold” In Nature 596.7873, 2021, pp. 583–589 DOI: 10.1038/s41586-021-03819-2
  20. Guo-Wei Wei “Protein Structure Prediction beyond AlphaFold” In Nature Machine Intelligence 1.8, 2019, pp. 336–337 DOI: 10.1038/s42256-019-0086-4
  21. “A Deep Learning Approach to Antibiotic Discovery” In Cell 180.4, 2020, pp. 688–702 DOI: 10.1016/j.cell.2020.01.021
  22. “Out-of-the-Box Deep Learning Prediction of Pharmaceutical Properties by Broadly Learned Knowledge-Based Molecular Representations” In Nature Machine Intelligence 3.4, 2021, pp. 334–343 DOI: 10.1038/s42256-021-00301-6
  23. Paul Callaghan “Translational Dynamics and Magnetic Resonance” Oxford University Press, 2011
  24. “T1–T2 Correlation Spectra Obtained Using a Fast Two-Dimensional Laplace Inversion” In Journal of Magnetic Resonance 154.2, 2002, pp. 261–268 DOI: 10.1006/jmre.2001.2474
  25. L. Venkataramanan, Yi-Qiao Song and M.D. Hurlimann “Solving Fredholm integrals of the First Kind with Tensor Product Structure in 2 and 2.5 Dimensions” In IEEE Transactions on Signal Processing 50.5, 2002, pp. 1017–1026 DOI: 10.1109/78.995059
  26. “Nuclear Magnetic Resonance T1–T2 Inversion with Double Objective Functions” In Journal of Magnetic Resonance 308, 2019, pp. 106562 DOI: 10.1016/j.jmr.2019.07.049
  27. Charles L Lawson and Richard J Hanson “Solving Least Squares Problems” SIAM, 1995
  28. Trevor Hastie, Robert Tibshirani and Jerome Friedman “The Elements of Statistical Learning” Springer, 2009
  29. “An Introduction to Statistical Learning” Springer, 2021
  30. A N Tikhonov and V Y Arsenin “Solutions of Ill-Posed Problems.” V. H. WinstonSons, 1978
  31. “A New Tikhonov Regularization Method” In Numerical Algorithms 59.3, 2012, pp. 433–445 DOI: 10.1007/s11075-011-9498-x
  32. Gene H Golub, Per Christian Hansen and Dianne P O’Leary “Tikhonov Regularization and Total Least Squares” In SIAM Journal on Imaging Sciences 21.1, 1999, pp. 185–194 DOI: 10.1137/s0895479897326432
  33. J Mitchell, T C Chandrasekera and L F Gladden “Numerical Estimation of Relaxation and Diffusion Distributions in Two Dimensions” In Progress in Nuclear Magnetic Resonance Spectroscopy 62, 2012, pp. 34–50 DOI: 10.1016/j.pnmrs.2011.07.002
  34. A Reci, A J Sederman and L F Gladden “Obtaining Sparse Distributions in 2D Inverse Problems” In Journal of Magnetic Resonance 281, 2017, pp. 188–198 DOI: 10.1016/j.jmr.2017.05.010
  35. A Reci, A J Sederman and L F Gladden “Retaining Both Discrete and Smooth Features in 1D and 2D NMR Relaxation and Diffusion Experiments” In Journal of Magnetic Resonance 284, 2017, pp. 39–47 DOI: 10.1016/j.jmr.2017.08.017
  36. “Generalized Cross-Validation as a Method of Hyperparameter Search for MTGV Regularization” In arXiv, 2023 DOI: 10.48550/arXiv.2311.11442
  37. Gene H Golub, Michael Heath and Grace Wahba “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter” In Technometrics 21.2, 1979, pp. 215 DOI: 10.2307/1268518
  38. You-Wei Wen and Raymond Honfu Chan “Using Generalized Cross Validation to Select Regularization Parameter for Total Variation Regularization Problems” In Inverse Problems & Imaging 12.5, 2018, pp. 1103–1120 DOI: 10.3934/ipi.2018046
  39. J P Butler, J A Reeds and S V Dawson “Estimating Solutions of First Kind Integral Equations with Nonnegative Constraints and Optimal Smoothing” In SIAM Journal on Numerical Analysis 18.3, 1981, pp. 381–397 DOI: 10.1137/0718025
  40. Mark Orr “Introduction to Radial Basis Function Networks” Centre for Cognitive Science, University of Edinburgh, 1996
  41. Xavier Glorot, Antoine Bordes and Yoshua Bengio “Deep Sparse Rectifier Neural Networks” In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics 15, pp. 315–323
  42. “Understanding the Difficulty of Training Deep Feedforward Neural Networks” In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics 9, 2010, pp. 249–256
  43. “On the Importance of Initialization and Momentum in Deep Learning” In Proceedings of the 30th International Conference on Machine Learning, 2013
  44. “All You Need Is a Good Init” In arXiv, 2016
  45. Diederik P Kingma and Jimmy Ba “Adam: A Method for Stochastic Optimization” In arXiv, 2014
  46. “The Loss Surfaces of Multilayer Networks” In arXiv, 2015 DOI: 10.48550/arXiv.1412.0233
  47. “Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors” In arXiv, 2012 DOI: 10.48550/arXiv.1207.0580
  48. Junyuan Xie, Linli Xu and Enhong Chen “Image Denoising and Inpainting with Deep Neural Networks” In Advances in Neural Information Processing Systems, 2012
  49. Olaf Ronneberger, Philipp Fischer and Thomas Brox “U-Net: Convolutional Networks for Biomedical Image Segmentation” In arXiv, 2015 DOI: 10.48550/arXiv.1505.04597
  50. Jonathan Long, Evan Shelhamer and Trevor Darrell “Fully Convolutional Networks for Semantic Segmentation” In arXiv, 2014 DOI: 10.48550/arXiv.1411.4038
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up